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聚类特征空间-具有自适应MeanShift的SURF描述符

[英]Clustering feature Space - SURF descriptors with Adaptive MeanShift

didnt find anything on internet. 没有在互联网上找到任何东西。 There have been some papers around recently about clustering feature space descriptors (such from SIFT/SURF) using the Mean Shift algo. 最近有一些关于使用均值漂移算法的特征空间描述符聚类的论文(例如SIFT / SURF)。 Does anybody have any links or any code/library/tip to actually cluster SURF descriptors? 是否有人有任何链接或任何代码/库/技巧来实际聚集SURF描述符? (Matlab/C++) (Matlab / C ++)

I've already tried to use the 1D Mean-Shift (which perfectly works on the locations of the points) and also some other mean shifts which were avaiable...though all seem to have problems with higher dimensions. 我已经尝试过使用1D均值平移(在点的位置上完美工作)以及其他一些可用的均值平移...尽管所有这些似乎都存在较大的尺寸问题。

Thanks in advance! 提前致谢!

Why are you using a 1D classification algorithm with a high-dimensional dataset? 为什么要对高维数据集使用一维分类算法? Mean-shift segmentation is an unsupervised classification task while SIFT and SURF are used to find keypoints in an image. 均值漂移分割是一项无监督的分类任务,而SIFT和SURF用于查找图像中的关键点。 There is only one mean-shift. 只有一个均值漂移。 There are other alterntives such as CAMshift but are mostly independent of mean-shift. 还有其他替代方案,例如CAMshift,但大多与均值漂移无关。 SURF and mean-shift are independent algorithms. SURF和均值漂移是独立的算法。 Therefores you will find no implementation with dependencies unless it is tailored for a specific application. 因此,除非为特定应用程序量身定制,否则您将找不到依赖项的实现。

Thereforemroe SIFT commonly employs a 128-dimensional EoH-based descriptor (similar dimensionality to the extended SURF descriptor) for a given keypoint. 因此,对于给定的关键点,SIFT通常使用基于128维EoH的描述符(与扩展的SURF描述符相似的维)。 If you are going to account for the local position of each pixel (x,y) you will have a 130 dimensional feature space, not 1D. 如果要考虑每个像素(x,y)的局部位置,则将具有130维特征空间,而不是1D维。

If you wish to categorise the edge information in an image, you should first localise the keypoints in an image using SIFT or SURF. 如果要对图像中的边缘信息进行分类,则应首先使用SIFT或SURF在图像中定位关键点。 Then use a concatenated vector of the EoH and pixel position as the input to the segmentation algorithm. 然后使用EoH和像素位置的级联向量作为分割算法的输入。 If you search on google or mathworks functions for an N-dimensional mean-shift algorithm you would have found one. 如果您在google或mathworks函数上搜索N维均值漂移算法,您将找到一个。 Its the same process for a 1D dataset so has no gain being hard coded for a 1D user case. 对于一维数据集,其处理过程相同,因此对于一维用户案例而言,没有任何收益被硬编码。 You would have also found that MATLAB's image toolbox already contains a SURF implementation. 您还会发现MATLAB的图像工具箱已经包含SURF实现。

Mean-Shift: http://www.mathworks.co.uk/matlabcentral/fileexchange/10161-mean-shift-clustering 均值漂移: http : //www.mathworks.co.uk/matlabcentral/fileexchange/10161-mean-shift-clustering

SURF: http://www.mathworks.co.uk/help/vision/examples/object-detection-in-a-cluttered-scene-using-point-feature-matching.html SURF: http : //www.mathworks.co.uk/help/vision/examples/object-detection-in-a-cluttered-scene-using-point-feature-matching.html

The C++ and MATLAB SIFT implementations are referenced on the original paper and it's site (A. Vedaldi, "An implementation of SIFT detector and descriptor", 2004). 在原始论文及其站点上引用了C ++和MATLAB SIFT实现(A. Vedaldi,“一种SIFT检测器和描述符的实现”,2004年)。

SIFT: http://www.robots.ox.ac.uk/~vedaldi/code/sift.html SIFT: http : //www.robots.ox.ac.uk/~vedaldi/code/sift.html

Original SURF paper: http://www.vision.ee.ethz.ch/~surf/eccv06.pdf SURF原始论文: http : //www.vision.ee.ethz.ch/~surf/eccv06.pdf

Original SIFT paper: http://www.robots.ox.ac.uk/~vedaldi/assets/sift/sift.pdf SIFT原始论文: http : //www.robots.ox.ac.uk/~vedaldi/assets/sift/sift.pdf

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